Device and method for computing depth velocity variations

ABSTRACT

Method, computer device and software for calculating a corrected temporal variation (dt 1 ) depth  or a corrected relative temporal variation (dt 1 /t 1 ) depth  of a first body wave based on a second body wave. The method includes receiving raw seismic data recorded with a receiver; calculating arrival-time variations for the first and second body waves; calculating first and second relative temporal variations for the first and second body waves; and correcting the first relative temporal variation based on the second relative temporal variation to obtain the corrected relative temporal variation or correcting the first temporal variation based on the second temporal variation to obtain the corrected temporal variation. The second wave is either a body wave or a surface wave.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority and benefit from Provisional PatentApplication No. 61/561,998, filed Nov. 21, 2011, for “Method to computedepth velocity variations in presence of surface velocity variations,”Provisional Patent Application No. 61/583,883, filed Jan. 6, 2012, for“Method to compute depth velocity variations in presence of surfacevelocity variations,” and Provisional Patent Application No. 61/586,339,filed Jan. 13, 2012, the entire contents of which are incorporated intheir entirety herein by reference.

BACKGROUND

1. Technical Field

Embodiments of the subject matter disclosed herein generally relate tomethods and systems and, more particularly, to mechanisms and techniquesfor computing subsurface parameters variations (e.g., velocity) atdesired depths.

2. Discussion of the Background

Marine seismic data acquisition and processing generate a profile(image) of the geophysical structure under the seafloor. While thisprofile does not provide an accurate location for oil and gasreservoirs, it suggests, to those trained in the field, the presence orabsence of them. Thus, providing a high-resolution image of thesubsurface is an ongoing process.

Generally, a seismic source is used to generate a seismic signal whichpropagates into the earth and it is at least partially reflected byvarious seismic reflectors in the subsurface. The reflected waves arerecorded by seismic receivers. The seismic receivers may be located onthe ocean bottom, close to the ocean bottom, below a surface of thewater, at the surface of the water, on the surface of the earth, or inboreholes in the earth. The recorded seismic data, e.g., travel-time,may be processed to yield information relating to the location of thesubsurface reflectors and the physical properties of the subsurfaceformations, e.g., to generate an image of the subsurface.

One problem when acquiring seismic data is that one or more portions ofthe medium (e.g., water) above the surveyed subsurface may have variablevelocities. This depth velocity variation creates inconsistenttravel-times between the seismic sources and the receivers. For example,as a result of the interaction between warm and cold currents whenperforming marine seismic surveying, the water velocity may varyrapidly, both temporally and spatially. Thus, the velocity variationsmay be large enough to have a detrimental effect on subsequent dataprocessing. For example, an oil and gas reservoir may be monitored basedon the velocity variations produced by the reservoir. If velocityvariations introduced by the warm and cold currents, above thereservoir, are stronger than the velocity variations generated by thereservoir itself, the reservoir cannot be monitored or the obtainedresults are misleading.

Water velocity variations can be related to the water temperature,salinity and depth. As discussed above, the water velocity changes haveimplications for seismic processing. Water velocity differences mayresult in dynamic differences between data in the combined datasets, andthese changes may affect the data processing, in particular, processeslike multiple attenuation, stacking and 3D migration. However, otherlayers in the substrate may introduce similar variations. For example,for a land survey, the upper layer (weather layer) may also introducethese variations.

There are methods in seismology that allow computing of fine relativevelocity variations (dV/V) in the subsurface. In these methods,correlations of noise records are used to reconstruct Green functionsbetweens pairs of receivers. Under certain hypotheses, it is possible tocompute velocity variations for the coda of the correlated signals asdescribed, for example, in Brenguier et al., “Towards forecastingvolcanic eruptinos using seismic noise,” Nature Geoscience, Volume: 1,Issue: 2, pages: 126-130, 2008.

In geophysics, ballistic paths of the reflection on the reservoir arepreferably used. The arrival-time delay of the corresponding wavelet iscomputed. If the reservoir properties are changing (e.g., oil or CO₂concentration, water injection, etc.), the velocity field is modifiedlocally and the arrival-times of the wavelets vary. Determining thevariation of the wavelet properties (e.g. arrival-times) allowsreservoir parameters monitoring.

However, for this method, the waves reflected on the reservoir can bevery noisy due to a weak intensity. If this is the case, theSignal-to-Noise Ratio (SNR) can be insufficient for velocity variationmonitoring.

The near-surface layer (i.e., the medium just below the surface) facesdaily and/or seasonal variations, called spurious variations, due tochanges in temperature, humidity, etc. These variations inducenear-surface velocity variations (i.e., noise), which can hide the deepvelocity tracked variations. If the wavelet delays induced by thenear-surface are greater than the delay due to the reservoir parametersvariations, it is not possible to accurately monitor the reservoir.

To improve the SNR, a non-rigid matching was proposed. Non-RigidMatching (NRM) is a method which estimates the change in two-way time(TWT) of geological features between two seismic volumes, possiblyacquired at two different times. The change in TWT may, e.g., be due toa change in velocity in the surveyed area, displacement of one or moregeological features, or a change in acquisition geometry (4D“acquisition footprint”). The method, a trace-by-trace matching,operates on pairs of collocated traces from the two surveys. For eachpair, a unique operator is designed to cause one trace of the pair tobetter match the other. A smoothness criterion is typically imposed toensure that the operators are spatially and temporally consistent. Thisenhances the contrast between the seismic responses related to changeswithin the reservoir and the areas where changes are due to acquisitionartifacts or noise.

Another method, implemented by the assignee (CGGVeritas) of this patentapplication, consists of burying the receivers and/or the sources. Theadvantages of this method are (1) a significant decrease in noise level,and (2) a protection against daily/seasonal variations because thedirect reflections do not propagate through the near-surface.

Although this last method works well, there are cases where it is notsufficient, in particular, when (1) surface wave energies are too high,and (2) the ghosts (or free-surface reflections) are mixed with theuseful signal. In this case, the seasonal/daily variations are present.

Regarding the velocity variations computations in the marine field,significant work has been performed to remove the water-layer velocityvariations between two successive acquisitions on a given area, forexample, “The impact of water-velocity variations on deepwater seismicdata,” The Leading Edge, 2003, U.S. Pat. No. 7,321,526, U.S. Pat. No.6,799,118.

U.S. Patent Publication No. 2007/0268780 discloses a method for removingmove-out computation uncertainties. This method uses a collection oftraces with similar offset, azimuth and common-depth-point (CDP).

However, all these methods consider only the water-layer velocityvariations' contribution removal. In other words, the existing methodsdo not consider the contribution removal of other layers, above thetargeted depth but below the water-layer. Further, some of the existingmethods describe an indirect delay computation or indirect velocitydetermination for compensating the spurious variations. However, thiscomputation requires first a move-out step. The methods also assume amodel (water-bottom depth, water-layer and earth velocity model). Themethods further assume slow variations of the water-layer, or use onlythe water-bottom reflection to correct the computation, and the methodsdo not take advantage of source and/or receiver arrays. The slowness isnot used to compute the incidence angles, and the methods do notconsider the case where it is not possible to recover the incidenceangles.

A method used in 4D land acquisition, the “cross equalization”technique, is described in, e.g., Ross et al. “Inside thecross-equalisation black box,” The Leading Edge, 1233-1240, 1996. Someimprovements to the technique to reduce amplitude bias were introducedby Rickett and Lumley, “A cross equalization processing flow foroff-the-shelf 4D seismic data,” 68^(th) Ann. Internat. Mtg. SEG ExpandedAbstract, 1998.

The method considers several stacks of the same area acquired atdifferent times. The 4D processing consists in searching time-lapsevariations at depth. However, this method has problems due to the staticvariations occurring at the near-surface, which hide the depthvariations. To correct this effect, a reference wavelet in a givenwindow is chosen at a first acquisition (signal s1). For a secondacquisition, a control wavelet is chosen in the same window (signal s2).The algorithm computes an operator A so that:As ₂ −s ₁≈0.

The operator A can be computed in the time or in the frequency domain.In the frequency domain, the following relation is obtained:A(ω))s ₂(ω))−s ₁(ω)≈0.

The operator A is supposed to contain the near-surface variationsbetween the times of the two acquisitions. By applying operator A to thewhole trace, the algorithm is able to compensate the near-surfacevariations' effect at depth.

This operation is performed after stack, and thus, it suffers from theapproximation due to the normal move-out (NMO) operation. Anotherdrawback is that the same operator A applies for all the traces havingthe same common mid-point but with different source and receiver points.This last issue is addressed by Meunier et al. “Determining acquisitionparameters time-lapse seismic recording,” 59th EAGE conference andExhibition, 1997. In the method proposed by Meunier, thecross-equalization is applied before NMO, between each source-receiverpair, leading to a surface-consistent correction.

To summarize the deficiencies of the existing methods, it is noted thatin the conventional 4D exploration, the time window used to compute the“cross-equalization” correction may contain several mixed wave arrivalswith different time-evolving variation; using buried sources is limitedby their low power so that the body wave reflected off the reservoirinterface (hereafter called useful reflection) suffers from a low SNR,which fails to provide an efficient tracking of velocity variations; andthe “cross-equalization” correction is performed after NMO correctionand requires a reliable velocity model to track fine velocityvariations.

Accordingly, it would be desirable to provide systems and methods thatavoid the afore-described problems and drawbacks.

SUMMARY

According to one exemplary embodiment, there is a method for calculatinga corrected temporal variation (dt₁)_(depth) or a corrected relativetemporal variation (dt₁/t₁)_(depth) of a body wave based on a givenwave. The method includes receiving raw seismic data recorded with areceiver, wherein the raw seismic data includes recordings for the bodywave and the given wave at various times; calculating a firstarrival-time variation (dt₁) for the body wave; calculating a secondarrival-time variation (dt₂) for the given wave; calculating a firstrelative temporal variation (dt₁/t₁) for the body wave based on thefirst arrival-time variation (dt₁); calculating a second relativetemporal variation (dt₂/t₂) for the given wave based on the secondarrival-time variation (dt₂); and correcting with a computing device thefirst relative temporal variation (dt₁/t₁) based on the second relativetemporal variation (dt₂/t₂) to obtain the corrected relative temporalvariation (dt₁/t₁)_(depth), or correcting with the computing device thefirst temporal variation (dt₁) based on the second temporal variation(dt₂) to obtain the corrected temporal variation (dt₁)_(depth). A bodywave is a wave that experiences at least one reflection before beingrecorded by the receiver, a surface wave is a wave that does notexperience any reflection between the source and the receiver, and thegiven wave is either the body wave or the surface wave.

According to another exemplary embodiment, there is a method forcalculating a corrected parameter variation (dp₁)_(depth) or a correctedrelative parameter variation (dp₁/t₁)_(depth) of a body wave based on agiven wave. The method includes receiving raw seismic data recorded witha receiver, wherein the raw seismic data includes recordings for thebody wave and the given wave at various times; applying an arrayprocessing algorithm to determine first and second relative parametervariations (dp₁/p₁) and (dp₂/p₂); and correcting the first relativeparameter variation (dp₁/p₁) based on the second relative parametervariation (dp₂/p₂) to obtain the corrected relative parameter variation(dp₁/p₁)_(depth), or correcting the first parameter variation (dp₁)based on the second parameter variation (dp₂) to obtain the correctedparameter variation (dp₁)_(depth). A body wave is a wave thatexperiences at least one reflection before being recorded by thereceiver, a surface wave is a wave that does not experience anyreflection, and the given wave is either the body wave or the surfacewave.

According to still another exemplary embodiment, there is a computingdevice for calculating a corrected temporal variation (dt₁)_(depth) or acorrected relative temporal variation (dt₁/t₁)_(depth) of a body wavebased on a given wave. The computing device includes an interfaceconfigured to receive raw seismic data recorded with a receiver, whereinthe raw seismic data includes recordings for the body wave and the givenwave at various times; and a processor connected to the interface. Theprocessor is configured to calculate a first arrival-time variation(dt₁) for the body wave, calculate a second arrival-time variation (dt₂)for the given wave, calculate a first relative temporal variation(dt₁/t₁) for the body wave based on the first arrival-time variation(dt₁), calculate a second relative temporal variation (dt₂/t₂) for thegiven wave based on the second arrival-time variation (dt₂), and correctthe first relative temporal variation (dt₁/t₁) based on the secondrelative temporal variation (dt₂/t₂) to obtain the corrected relativetemporal variation (dt₁/t₁)_(depth), or correct the first temporalvariation (dt₁) based on the second temporal variation (dt₂) to obtainthe corrected temporal variation (dt₁)_(depth). The body wave is a wavethat experiences at least one reflection before being recorded by thereceiver, a surface wave is a wave that does not experience anyreflection, and the given wave is either the body wave or the surfacewave.

According to still another exemplary embodiment, there is acomputer-readable storing medium including computer executableinstructions, wherein the instructions, when executed by a processor,implement instructions for calculating a corrected temporal variation(dt₁)_(depth) or a corrected relative temporal variation(dt₁/t₁)_(depth) of a body wave based on a given wave. The instructionsimplement the steps noted above.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate one or more embodiments and,together with the description, explain these embodiments. In thedrawings:

FIG. 1 is a schematic diagram of a source that generates body waves andsurface waves according to an exemplary embodiment;

FIG. 2 is a schematic diagram of an experimental set up according to anexemplary embodiment;

FIG. 3 is a graph of measured relative temporal variations for threedifferent waves according to an exemplary embodiment;

FIGS. 4A-D illustrate how a velocity variation affects a path variationaccording to an exemplary embodiment;

FIGS. 5A-B illustrate the effect of the path variation according to anexemplary embodiment;

FIG. 6 is a graph of a velocity variation function of the depthaccording to an exemplary embodiment;

FIG. 7 is an illustration of a path change due to a velocity change in agiven layer according to an exemplary embodiment;

FIGS. 8A-E are graphs illustrating raw data, slowness and signalsrecorded for various waves according to an exemplary embodiment;

FIGS. 9A-D are graphs illustrating a temperature evolution of a medium,a timing for turning on and off heaters, and relative temporalvariations for corrected and uncorrected waves according to an exemplaryembodiment;

FIG. 10 is a flowchart of a method for correcting a relative temporalvariation of a first body wave based on a second body wave according toan exemplary embodiment;

FIG. 11 is a graph illustrating relative temporal variations of a bodywave versus a surface wave according to an exemplary embodiment;

FIG. 12 is a flowchart of a method for correcting a relative temporalvariation of a body wave based on a surface wave according to anexemplary embodiment;

FIG. 13 is a schematic diagram illustrating multiples according to anexemplary embodiment;

FIGS. 14A-D are graphs illustrating relative parameter variations forvarious parameters according to an exemplary embodiment; and

FIG. 15 is a schematic diagram of a computing device in which one ormore of the exemplary embodiments may be implemented.

DETAILED DESCRIPTION

The following description of the exemplary embodiments refers to theaccompanying drawings. The same reference numbers in different drawingsidentify the same or similar elements. The following detaileddescription does not limit the invention. Instead, the scope of theinvention is defined by the appended claims. The following embodimentsare discussed, for simplicity, with regard to a method of double beamforming (DBF) to separate recorded waves and to compute a correction tocompensate relative parameter variations and isolate variations producedby a volume of interest in the subsurface. However, the embodiments tobe discussed next are not limited to DBF, but may be used with otheralgorithms for separating incoming waves. Further, the embodimentsdiscussed next equally apply to compute a correction to compensate aparameter variation not only a relative parameter variation.Furthermore, although the exemplary embodiments focus on a relativetemporal variation for a given wave, they equally apply to any otherrelative subsurface parameter variation or to any subsurface parametervariation. In other words, the subsurface parameter may be the time, thevelocity, the amplitude, the slowness, the azimuth and the exemplaryembodiments may correct their variations or their relative variations atdesired depths. However, for simplicity, the following exemplaryembodiments mainly refer to the relative temporal variation.

Reference throughout the specification to “one embodiment” or “anembodiment” means that a particular feature, structure or characteristicdescribed in connection with an embodiment is included in at least oneembodiment of the subject matter disclosed. Thus, the appearance of thephrases “in one embodiment” or “in an embodiment” in various placesthroughout the specification is not necessarily referring to the sameembodiment. Further, the particular features, structures orcharacteristics may be combined in any suitable manner in one or moreembodiments.

According to an exemplary embodiment, a novel algorithm to be discussednext corrects a parameter variation of a first wave (a desiredreflection) based on a parameter variation of a second wave (anintermediate wave) to isolate variations generated by the target, e.g.,monitored reservoir. In other words, the wave of interest is affected byparameter variations from a target that is desired to be monitored, butalso from a layer above the target. The parameter variation introducedby the layer above the target may be considered noise that needs to beestimated and removed. Thus, a second wave that travels through thelayer above the target but not through the target is used to evaluateand compensate the noise.

It is noted that a simplified seismic survey 10 is illustrated in FIG. 1and includes a source S and a receiver R provided on the Earth's surface12. The target reservoir 14 is separated from the surface 12 by a lowvelocity layer 16 and a high velocity layer 18. A layer 20 having adepth h, less than a depth of the low velocity layer 16, may beresponsible for velocity variations (noise) that may mask the velocityvariations generated by the target 14.

The layer 20 is traditionally referred to as a weather layer and thislayer is impacted by spurious variations that need to be removed fromthe recorded waves. The variations are spurious in the weather layer 20because there are many parameters that may impact this layer, e.g.,daily and/or seasonal variations of temperature, humidity, pH, etc.

Thus, the seismic receiver R records waves affected by the variationsproduced by the target 14 and also by the weather layer 20 (or otherlayers situated between the target and the receiver). The intermediatewave can be a surface wave 22 (i.e., a wave that propagates directlyfrom the source S to the receiver R with no reflections) or a body wave(24 or 26) reflecting in the medium below the receiver R. One example isa reflecting wave 24 that reflects at an interface 28 between the lowvelocity layer 16 and the high velocity layer 18, and another example ofa body wave is a reflecting wave 26 that reflects from the target 14.The intermediate wave needs to be impacted by the velocity variationsthat are intended to be estimated (produced by layer 20) and removedfrom the seismic data recorded by the receiver R.

If the SNR is insufficient for a single source and a single receiver,the algorithm relies on data collected from a source and/or receiverarray, where an array includes plural elements (sources or receivers).For this case, the DBF algorithm (known in the art and not describedherein) allows selection of waves with regard to their source and/orreceiver azimuth and/or slowness. With a source array that includes Nsources and a receiver array that includes M receivers, the SNR gain maybe √{square root over (N·M)}.

According to a first novel algorithm, for a given target, paths delaysare computed using the arrival-time of the recorded wavelets. Waveletarrival-time computation methods are known in the art and, thus, notrepeated herein. The time arrival of a given wave is compared with areference wavelet arrival-time. The reference wavelet may be the mean ofthe considered wavelet for a certain number of measures after windowing.The arrival-time may be estimated using a peak detection in the timedomain or a phase difference computation in the phase domain after FastFourier Transform. The arrival-time may be optimized in the time domainwith, for example, the gradient algorithm. The gradient algorithmminimizes the L2 norm of the difference between the current windowedwavelet shifted by the tested travel-time variation and the referencewindowed wavelet.

If dt is the arrival-time variation of a wavelet arriving at time t, itcan be shown (to be discussed later in more detail) that the relativetemporal variations dt/t are proportional to the relative velocityvariations dV/V. To validate that a surface wave or a body wave (theintermediate wave) has arrival-time variations correlated with a bodywave reflected on the target (the desired reflection), a small-scaleexperiment was carried out as now described. It is noted that thiscorrelation is necessary for removing the unwanted velocity variationsfrom the desired reflection.

The experiment uses, as shown in FIG. 2, a two-layer agar gel (similarto the layers 16 and 18 in FIG. 1) through which elastic wavespropagate. The elastic waves are generated at the source S (e.g., usinga piezoelectric source made of 5×5 emitters) and the reflections of thewaves are recorded with the receiver R (e.g., a laser receiver arrayhaving 5×5 elements). For example, the agar gel size may be 450 mm×150mm×90 mm. Velocity variations are generated in this experimental set upby placing a first thermal heater H1 (e.g., a resistor) in layer 20 anda second thermal heater H2 in the high velocity layer. Temperatureprobes T1 to T3 are placed in each layer to determine temperaturevariations. The temperature probes may be placed at depths of 0, 3, and8 cm. Six parameters may be monitored, e.g., travel-time, amplitude,source and receiver slownesses, source and receiver azimuth for threesurface waves and three body waves. An emission/reception sequence isrepeated over a given time period, and velocity variations can beobserved through wavelet arrival-time measurements. The desiredreflection is considered wave 26 in FIG. 1, and the intermediate wave isone of the waves 24 or 22 in FIG. 1.

FIG. 3 illustrates relative temporal variations dt/t for the three waves22, 24 and 26 plotted as a function of time. It is noted that thisexperiment indicates that the relative temporal variations arecorrelated among themselves, which indicate that either wave 22 or wave24 may be used to estimate the temporal variations introduced by layer20 to remove it from wave 26. Thus, it is possible to compute depthvelocity variations despite velocity variations impacting the weatherlayer. In fact, the novel algorithm to be discussed next is capable ofremoving velocity variations introduced by any layer above the target.

Returning to the assumed proportionality between the relative temporalvariations dt/t and the relative velocity variations dV/V, next derivedis the relation between these two quantities. It is noted that—due tospurious variations—the wave velocities vary from V to V+dV in thenear-surface, between depth zero and h, in FIG. 1. This variationimpacts the delay of all the waves propagating through the near-surface.Although this embodiment refers to near-surface, the novel algorithm isapplicable to other layers, deeper than the near-surface. Thus, in thefollowing, it will be shown that the relative temporal variations ofwave 26 are linked to the spurious variations in the near-surface andthe velocity variations introduced by layer 20 may be estimated from therelative temporal variations of another wave (e.g., wave 24 or wave 22).In the following, for simplicity in terms of the mathematical notations,wave 22 is associated with index “a,” wave 24 is associated with index“b,” and wave 26 is associated with index “c.”

Because wave 22 is a surface wave, its path is always the same, i.e.,between points S and R. Its arrival-time is given by t_(a)=D/V if D isconsidered to be the distance between S and R with no reflection. Due tothe velocity variation dV in the near-surface layer, the time arrivalvariation becomes t_(a)+dt_(a)=D/(V+dV). From the above two expressions,it can be determined that at first order:

$\frac{{dt}_{a}}{t_{a}} = {- {\frac{dV}{V}.}}$

If the arrival-time variation of wave 24 is written over half offsetD/2, two effects need to be taken into account: (1) the velocityvariation along the path, and (2) the path variation due to the velocityvariation. The path variation (the second effect) originates from thevelocity variation as discussed now with regard to FIGS. 4A-D. FIG. 4Aillustrates the trajectory 24 a of wave 24 through a single layer, andFIG. 4B illustrates the trajectory of the same wave through two layershaving a total depth equal to the layer of FIG. 4A. Similarly, FIGS. 4Cand 4D show the trajectory 26 a of wave 26 through two and three layers,respectively. In FIGS. 4B and D, the top layer introduced velocityvariations. According to Snell-Descartes' law, the expressionsin(θ)/V=constant is true at a given interface (where θ is the incidenceangle). As the velocity V varies in the top layer, so does θ, whichleads to path modifications as illustrated in FIG. 4B for wave 24 andFIG. 4D for wave 26.

In the following, it is assumed that the relative temporal variation dueto the second effect (i.e., path variation) is not significant comparedto the first one (i.e., velocity variation). Thus, the path variation isignored in the following. This hypothesis can be validated numericallyas follows. If the dV/V variation is considered to be around +/−5% for adepth h between 5 mm up to 0, for each value of dV/V and h, the relativetemporal variation due to the first and the second effect is computed.

FIGS. 5A-B illustrate the results of this computation, with FIG. 5Aillustrating the impact of the first effect and FIG. 5B illustrating theimpact of the second effect. It is noted that the ratio between therelative temporal variation and the speed variation stays below 0.2% forwave 24 and 0.6% for wave 26. This means that for a velocity variationlower than 5%, the corresponding path variation can be neglected. Thus,for simplicity, only the velocity variation along the initial path isconsidered in the following. However, the algorithm may be extended toalso take into account the second effect (path variation).

Further, it is noted that in practice, the velocity variation is not soabrupt, but rather is a smooth velocity profile with regard to a depth(z axis) as illustrated in FIG. 6A. The same assumption may be made foreach sub-layer of the near-surface, when assuming a locally constantvelocity. Thus, whatever the velocity profile, it is safe to assume thatthe propagation path is constant.

Considering now a sub-layer 20 a of the layer 20 at a depth z asillustrated in FIG. 7, a velocity of the wave varies in this layer fromV(z) to V(z)+δV(z). Over this depth, the path length of wave 24 is:δs _(b) =δz/cos(θ_(b))After the velocity change, the path time variation is:

${\delta\;{t_{b}(z)}} = {{{\frac{\delta\; z}{\cos\left( \theta_{b} \right)}\frac{1}{{V(z)} + {{dV}(z)}}} - {\frac{\delta\; z}{\cos\left( \theta_{b} \right)}\frac{1}{V(z)}}} = {{- \frac{\delta\; z}{\cos\left( \theta_{b} \right)}} \cdot {\frac{{dV}(z)}{V(z)}.}}}$For wave 26, the path length change is given by:

${\delta\;{t_{c}(z)}} = {{- \frac{\delta\; z}{\cos\left( \theta_{c} \right)}} \cdot {\frac{d\;{V(z)}}{V(z)}.}}$It is noted that:

${\delta\;{t_{c}(z)}} = {\frac{\cos\left( \theta_{b} \right)}{\cos\left( \theta_{c} \right)}\delta\;{{t_{b}(z)}.}}$As the path is constant, it is possible to integrate the quantity overthe complete path without taking into account the angle variation. Thus,as the velocity is varying only between depth 0 and h1 with 0<h₁≦h, thefollowing expression is obtained:

${d\; t_{c}} = {{\int_{0}^{h_{1}}{\delta\;{t_{c}(z)}\delta\; z}} = {{\frac{\cos\left( \theta_{b} \right)}{\cos\left( \theta_{c} \right)} \cdot {\int_{0}^{h_{1}}{\delta\;{t_{c}(z)}\delta\; z}}} = {\frac{\cos\left( \theta_{b} \right)}{\cos\left( \theta_{c} \right)}d\;{t_{b}.}}}}$This means that, for a velocity variation impacting only thenear-surface, the relative temporal variation of wave 26 can bedetermined from the relative temporal variation of wave 24 according to:

$\left( \frac{d\; t_{c}}{t_{c}} \right)_{{near}\mspace{14mu}{surface}} = {\frac{t_{b}}{t_{c}} \cdot \frac{\cos\left( \theta_{b} \right)}{\cos\left( \theta_{c} \right)} \cdot {\left( \frac{d\; t_{b}}{t_{b}} \right).}}$Then, if wave 26 is also impacted by a velocity variation at a givendepth, the relative temporal variation

$\left( \frac{d\; t_{c}}{t_{c}} \right)_{depth}$at that depth is given by:

$\begin{matrix}\begin{matrix}{\left( \frac{d\; t_{c}}{t_{c}} \right)_{depth} = {\left( \frac{d\; t_{c}}{t_{c}} \right) - \left( \frac{d\; t_{c}}{t_{c}} \right)_{{near}\; - {surface}}}} \\{= {\left( \frac{d\; t_{c}}{t_{c}} \right) - {\frac{t_{b}}{t_{c}} \cdot \frac{\cos\left( \theta_{b} \right)}{\cos\left( \theta_{c} \right)} \cdot \left( \frac{d\; t_{b}}{t_{b}} \right)}}} \\{{= {\left( \frac{d\; t_{c}}{t_{c}} \right) - {r \cdot \left( \frac{d\; t_{b}}{t_{b}} \right)}}},}\end{matrix} & (1)\end{matrix}$where r is

$\frac{t_{b}}{t_{c}} \cdot {\frac{\cos\left( \theta_{b} \right)}{\cos\left( \theta_{c} \right)}.}$In other words, dt_(c)/t_(c) is the total relative temporal variationdue to both the near-surface layer ((dt_(c)/t_(c))_(near-surface)) andthe target ((dt_(c)/t_(c))_(depth)). A similar relation can be derivedfor the temporal variation instead of the relative temporal variation:

$\begin{matrix}{\left( {d\; t_{c}} \right)_{depth} = {{{d\; t_{c}} - \left( {d\; t_{c}} \right)_{{near}\; - {surface}}} = {{{d\; t_{c}} - {{\frac{\cos\left( \theta_{b} \right)}{\cos\left( \theta_{c} \right)} \cdot d}\; t_{b}}} = {{d\; t_{c}} - {{r \cdot d}\;{t_{b}.}}}}}} & (2)\end{matrix}$It is noted that an equation for a subsurface parameter variation dx,where x is considered to be a generic subsurface parameter, can bederived from an equation for the corresponding relative subsurfaceparameter variation dx/x by multiplying the entire equation for therelative subsurface parameter variation by x.

Thus, equations valid for relative parameter variations can be similarlyadapted for parameter variation. For simplicity, equation (1) is used todetermine the relative temporal variations for waves 22, 24 and 26 inthe above-noted experiment. More specifically, the receiver records thesignals coming from the source array over a 24-hour period, with ameasurement taking place every 20 minutes. One point-to-point tracebetween the center of the source array and the center of the receiverarray is illustrated in FIG. 8A. This corresponds to the raw data. Theraw data is processed with the DBF algorithm to produce the slownessmapping illustrated in FIG. 8B.

Using the DBF algorithm, three wavelets corresponding to waves 22, 24and 26 are extracted using the slowness mapping. The wavelets areillustrated in FIGS. 8C, D and E. Using the slowness map of FIG. 8B, DBFallows recovery of the incidence angles for each wave due to therelation u=sin(θ)/V for the body waves, knowing that V=1/u for thesurface wave, and u being the slowness.

Next, the temporal variations dt, or relative temporal variations (dt/t)of the three waves are measured while activating an appropriate heaterat the surface (to simulate spurious variations) and/or in depth (tosimulate the desired variations produced by the target). FIG. 9Aillustrates the temperature 40 of the medium at the surface, thetemperature 42 at depth 3 cm and the temperature 44 at the bottom. FIG.9B illustrates the relative temporal variations 46, 48 and 50corresponding to waves 22, 24 and 26, respectively, and FIG. 9Cillustrates the first heater activation schedule 52 and the secondheater activation schedule 54. FIG. 9D illustrates the depth heateractivation 56, the relative temporal variation 58 for wave 26 (which isidentical to curve 50 in FIG. 9B because no correction is applied), therelative temporal variation 60 for wave 26 corrected based on wave 24,and the relative temporal variation 62 for wave 26 corrected based onwave 22.

It is observed that the relative temporal variation 60 is calculatedbased on equation (1). However, the relative temporal variation 62cannot be calculated with equation (1) because the incidence angle iszero for a surface wave. Thus, another novel method, to be discussedlater, is used to calculate the relative temporal variation 62.

Further, it is observed that if the depth heater is activated as shownin FIG. 9C, at hour 16, only the relative temporal variation dt/t of thewave 26 is impacted. However, if the near-surface layer is heated, allthree waves are impacted as shown in FIG. 9B, at hour 4. Then, if boththe depth and surface heaters are simultaneously activated, all threewaves' arrival-times are affected.

The novel algorithm is capable of discriminating for wave 26 the impactof the depth heating from the impact of the surface heating by applyingequation (1), as shown in FIG. 9D by curve 62.

In a practical case, another simplification may be implemented. The pathof intermediate wave 24 may be similar to the path of wave 26. Forexample, wave 24 can be the reflection over the top of the reservoir,while wave 26 may be the reflection within the reservoir. In this case,with the notation of FIGS. 4A-D, the following approximations hold:h ₂ >>h, h ₂ >>h ₁ and θ_(c)≈θ_(b).With these assumptions, equation (1) becomes:

$\left( \frac{d\; t_{c}}{t_{c}} \right)_{depth} = {\left( \frac{d\; t_{c}}{t_{c}} \right) - {\frac{t_{b}}{t_{c}} \cdot {\left( \frac{d\; t_{b}}{t_{b}} \right).}}}$

One or more of the above-discussed embodiments may be implemented as amethod, for example, in a computing device. Then, the method calculatesa corrected temporal variation (dt₁)_(depth) or a corrected relativetemporal variation (dt₁/t₁)_(depth) of a first body wave (26) based on asecond body wave (24) as illustrated in FIG. 10. The method includes astep 1000 of receiving raw seismic data recorded with a receiver,wherein the raw seismic data includes recordings for the first andsecond body waves at various times; a step 1002 of calculating a firstarrival-time variation (dt₁) for the first body wave (26); a step 1004of calculating a second arrival-time variation (dt₂) for the second bodywave (24); a step 1006 of calculating a first relative temporalvariation (dt₁/t₁) for the first body wave (26) based on the firstarrival-time variation (dt₁); a step 1008 of calculating a secondrelative temporal variation (dt₂/t₂) for the second body wave (24) basedon the second arrival-time variation (dt₂); and a step 1010 ofcorrecting in a computing device the first relative temporal variation(dt₁/t₁) based on the second relative temporal variation (dt₂/t₂) toobtain the corrected relative temporal variation (dt₁/t₁)_(depth). Themethod may be modified to correct in the computing device the firsttemporal variation (dt₁) based on the second temporal variation (dt₂) toobtain the corrected temporal variation (dt₁)_(depth). If this last stepis performed, then steps 1006 and 1008 may not be needed.

Various additional steps may be envisioned as, for example, calculatinga relative velocity variation (dV/V) in the subsurface based on thecorrected relative temporal variation (dt₁/t₁)_(depth), and monitoringchanges in a target present in a substrate based on the correctedrelative temporal variation (dt₁/t₁)_(depth).

Next, another novel algorithm for correcting the arrival-time of aseismic wave is discussed. This novel algorithm is capable of correctingthe arrival-time of a seismic wave even when equations (1) and (2)cannot be used. For example, if an incidence angle is zero, as for wave22, equation (1) cannot work. This may be true when no body wave isavailable to evaluate the near-surface variations or deeper variationsor when the two-layers model of FIG. 7 is not a sufficient approximationof the reality. In this case, the arrival-time correction may be madedirectly from any wave, including e.g., the surface wave perturbations.This approach assumes that the travel-time variations of thebottom-reflected wave 26 are proportional to the travel-time variationsof any wave experiencing the near-surface perturbations, including thesurface wave 22. To check this assumption, the relative temporalvariations dt/t of wave 26 are plotted as a function of the relativetemporal variations dt/t of wave 22, as illustrated in FIG. 11.

The various points in FIG. 11 do not show a unique linear relation.However, the graph shows the data being distributed along several lines.For example, quasi-vertical lines 80 imply that only wave 26 is affectedby time-arrival variations while wave 22 is not. This data is probablylinked to variations at depth, and this data is not considered toestablish a linear relation because the focus is on the near-surfacevariations experienced by all the waves.

Considering the line 82 passing through the origin and having a slope ofabout 0.5, many data points are on or about this line, which suggestthat both waves 22 and 26 are affected by common relative temporalvariations. For this data, only the near-surface is impacted by velocityfluctuations. The linear relationship between the relative temporalvariations of waves 22 and 26 (triangles in FIG. 11) best fit a slope rthat is equal to, for example, 0.4. The relative temporal variation forwave 26 corrected based on wave 22 is illustrated by curve 62 in FIG.9D.

Curve 62 has been calculated based on a first approximation as:

$\begin{matrix}{\left( \frac{d\; t_{c}}{t_{c}} \right)_{depth} = {\left( \frac{d\; t_{c}}{t_{c}} \right) - {r \cdot {\left( \frac{d\; t_{a}}{t_{a}} \right).}}}} & (3)\end{matrix}$

Applying the time correction to the bottom-reflected wave 26 measurementproduces curve 62, which accurately describes the true depth relativetemporal variation 58 in FIG. 9D. Thus, by computing the linearcoefficient r between travel-time perturbations of the body wave versusany other wave (which can be a surface wave or a body wave), thesubsurface effects can be removed from any body wave travel-times.

Comparing the body wave versus body wave (first novel algorithm, seecurve 60) and the surface wave versus body wave (second novel algorithm,see curve 62) correction techniques, some differences are noticeable.These differences can be due to some imperfections in the acquisitionand/or processing chain. However, it is noted that during a quiet periodbetween hours 8 and 10 (see FIG. 9D), these imperfections are limited to0.1% of the travel-time perturbations. Further, in the second method,the time dependency is removed as the method identifies a line in across-plot.

Another advantage of the second method is that it can work without array(of sources and/or receivers) beamforming if the SNR is high enoughbecause it does not use the incidence angle value. Both novel methodsare valuable for both land and marine data.

The second novel algorithm may be implemented as a method in a computingdevice as discussed next. The method, as illustrated in FIG. 12,calculates a corrected relative temporal variation (dt₁/t₁)_(depth) of abody wave (26) based on a surface wave (22). The method includes a step1200 of receiving raw seismic data recorded with a receiver, wherein theraw seismic data includes recordings for the body wave and the surfacewave at various times; a step 1202 of calculating a first arrival-timevariation (dt₁) for the body wave (26); a step 1204 of calculating asecond arrival-time variation (dt₂) for another wave (which can be asurface wave 22); a step 1206 of calculating a first relative temporalvariation (dt₁/t₁) for the body wave (26) based on the firstarrival-time variation (dt₁); a step 1208 of calculating a secondrelative temporal variation (dt₂/t₂) for the another wave (22) based onthe second arrival-time variation (dt₂); and a step 1210 of correctingwith a computing device the first relative temporal variation (dt₁/t₁)based on the second relative temporal variation (dt₂/t₂) to obtain thecorrected relative temporal variation (dt₁/t₁)_(depth). Similar to themethod illustrated in FIG. 10, if the corrected temporal variation(dt₁)_(depth) is intended to be determined, steps 1206 and 1208 may notbe needed and step 1210 may be modified to correct with the computingdevice the first temporal variation (dt₁) based on the second temporalvariation (dt₂) to obtain the corrected temporal variation(dt₁)_(depth). A body wave is a wave that experiences at least onereflection before being recorded by the receiver, and a surface wave isa wave that does not experience any reflection between the source andthe receiver.

Next, some enhancements to the above novel methods are discussed. Whenequation (1) was derived, it was asserted that an incidence angle is thesame for the source and receiver sides. However, if this is not thecase, a new equation can be derived to take into account this lack ofsymmetry. Considering waves 24 and 26 of FIG. 1, it can be shown thatthe arrival-time correction for depth is given by:

$\begin{matrix}{{\left( \frac{d\; t_{c}}{t_{c}} \right)_{depth} = {\left( \frac{d\; t_{c}}{t_{c}} \right) - {\frac{t_{b}}{\propto_{s}t_{c}} \cdot \frac{\cos\left( \theta_{b,{source}} \right)}{\cos\left( \theta_{c,{source}} \right)} \cdot \left( \frac{d\; t_{b}}{t_{b}} \right)} - {\frac{t_{b}}{\propto_{r}t_{c}} \cdot \frac{\cos\left( \theta_{b,{receiver}} \right)}{\cos\left( \theta_{c,{receiver}} \right)} \cdot \left( \frac{d\; t_{b}}{t_{b}} \right)}}},} & (3)\end{matrix}$where α_(s) and α_(r) are two coefficients that take into account thepath differences at the source and receiver sides. These twocoefficients are given by:α_(s)=1+cos(θ_(b,source))/cos(θ_(b,receiver)) andα_(r)=1+cos(θ_(b,receiver))/cos(θ_(b,source)).

Another aspect related to the novel algorithms is related to the effectof the frequency. It is known that a wave that propagates through amedium may experience dispersion, i.e., waves of different wavelengthstraveling at different phase speeds through the same medium. It is notedthat the coefficient r computed using either novel algorithm may varywith the frequency. Consider the following notations: c(t) is thetime-domain representation of the wavelet of interest at a given depth(wave 26 in FIG. 1), c₀(t) is the time-domain reference of the waveletof interest at the given depth, b(t) is the time-domain representationof the intermediate wavelet (wave 24 in FIG. 1), and b₀(t) is thetime-domain reference of the intermediate wavelet of interest.

If the beamforming method is used, c(t), c₀(t), b(t), and b₀(t)represent the wavelets computed using beamforming. Consider that in thefrequency domain, C(ω), C₀(ω), B(ω) and B₀(ω) are the Fourier transformsof c(t), c₀(t), b(t), and b₀(t), respectively. The novel algorithmsdiscussed above compute the coefficient r to link the near-surfacevariations of two different waves, e.g., waves 24 and 26. Thearrival-time differences of waves 24 and 26 are considered to be θ_(b)and θ_(c), respectively. These arrival-time differences are computedwith regard to the reference wave so that:B(ω)=e ^(iωθb) ·B ₀(ω) and C(ω)=e ^(iωθc) ·C ₀(ω).

For wave 26, if the arrival-time difference due to velocity variationsat the given depth is δ and the arrival-time difference due tonear-surface velocity variations is θ, then:C(ω)=e ^(iω(θ+δ)) ·C ₀(ω).

Using the novel algorithms, it is possible to compute r so thatθ=rθ_(b). Then, it can be determined that δ=θ_(c)−rθ_(b).

If the waves' behavior vary significantly with the frequency, it ispossible to extend the novel algorithms for different frequencies bycomputing the coefficient r and the delay to be frequency-dependant.Because r is computed using the same process and for each frequency, itis possible to have:B(ω)=e ^(iωθb(ω)) B ₀(ω) and C(ω)=e ^(iωθc(ω)) C ₀(ω).

Thus, applying this expression to the arrival-time differences, it canbe shown that: δ(ω)=θ_(c)(ω)−r(ω)·θ_(b)(ω), i.e., the arrival-timedifferences are frequency-dependant.

Embodiments discussed above have assumed that the arrival-timedifferences are calculated before NMO. However, it is possible tocalculate the arrival-time differences after NMO correction, asdiscussed next. In other words, the new method computes the travel-timecoefficient after NMO or after stack (i.e., without beamforming). Usinga traditional cross-equalization formalism (e.g., developed by RickettJ. and Lumley D. E., “A cross equalization processing flow foroff-the-shelf 4D seismic data,” 1998, 68th Ann. Internat. Mtg. SEGExpanded Abstract), a cross-equalization operator A is computed in thefrequency domain so that:A(ω)s ₂(ω)−s ₁(ω)=|A(ω)|e ^(iθ(ω)) s ₂(ω)−s ₁(ω)≈0,where θ(ω) is the phase computed to compensate the near-surface velocityfluctuations at frequency ω.

To take into account some imperfections (e.g., velocity model), thecross-equalization operator A can be corrected and rewritten as Baccording to:B(ω)=|A(ω)|e ^(irθ(ω)),where r can be computed according to the first or second novelalgorithms illustrated in FIGS. 10 and 12. In another application, afrequency-dependant coefficient can also be used so that:B(ω)=|A(ω)|e ^(ir(ω)θ(ω)).If all the cross-equalization assumptions are filled, the r coefficientshould be equal to 1 and the correction is not needed.

According to another exemplary embodiment, the novel algorithms may beadapted to take into account multiple reflection relative temporalvariations. A multiple is known as a signal that propagates back andforth between various layers of the substructure before being recordedby the receiver. An example of a multiple 100 is shown in FIG. 13. Inthis case, the multiple 100 may cross several times the near-surface(e.g., n times). FIG. 13 illustrates the case where n=2. Then, therelative temporal variations linked to the near-surface are multipliedby n.

In this case, equation (1) becomes:

$\begin{matrix}{\left( \frac{d\; t_{c}}{t_{c}} \right)_{depth} = {{\left( \frac{d\; t_{c}}{t_{c}} \right) - \left( \frac{d\; t_{c}}{t_{c}} \right)_{{near}\; - {surface}}} = {\left( \frac{d\; t_{c}}{t_{c}} \right) - {\frac{t_{b}}{n\; t_{c}} \cdot \frac{\cos\left( \theta_{b} \right)}{\cos\left( \theta_{c} \right)} \cdot \left( \frac{d\; t_{b}}{t_{b}} \right)}}}} & (4)\end{matrix}$and this equation may be extended to the case of non-symmetricalincidence angle in a manner similar to equation (3).

The above-discussed novel algorithms are appropriate not only forrelative temporal variations but also for amplitude, source and receiverslowness (or incidence angle), source and receiver azimuth variations.It can be shown that amplitude variations, slownesses variations andazimuth variations are linked to velocity variations. This observationis supported by the experiment described in FIG. 2 in which, e.g., asurface wave reflecting at the edge of the experimental device generatesthe results plotted in FIG. 14A-D. FIG. 14A illustrates the recordeddata (raw data), FIG. 14B illustrates the relative temporal variationdt/t and the amplitude variation divided by 10, FIG. 14C illustratessource slowness variation 102 and the receiver slowness variation 104,and FIG. 14D illustrates the receiver azimuth variation 106 and thesource azimuth variation 108.

Thus, using beamforming and the novel algorithms discussed above, sixparameters can be computed for each wave. These parameters can be thetime-arrival, the amplitude, the source slowness (or incidence angle ifthe surface velocity is known), the receiver slowness (or incidenceangle if the surface velocity is known), the source azimuth, and thereceiver azimuth.

Further, the algorithms discussed herein for monitoring parameters withbeamforming and compensating the variations due to the near-surfaceconditions or deeper conditions to deduce their variations at a desireddepth can be extended. For example, for the slowness S, it is possibleto compute another linear coefficient r_(s) so that:

$\begin{matrix}{\left( \frac{d\; S_{c}}{S_{c}} \right)_{depth} = {\left( \frac{d\; S_{c}}{S_{c}} \right) - {r_{s} \cdot \left( \frac{d\; S_{b}}{S_{b}} \right)}}} & (5)\end{matrix}$

The same equation may be used for the amplitude, source/receiverslownesses (or incidence angle), and the source/receiver azimuths. Inaddition, the above computations and corrections may be modified tobecome frequency-dependant as already discussed above.

Exemplary embodiments discussed above for computing the various waveparameters like amplitudes, slownesses, incidence angles or azimuths,etc. assumed the use of DBF technique. However, the above exemplaryembodiments are not limited by DBF and other array processing techniquesmay be used, such as Minimum Variance Distortionless Response (MVDR),Linearly Constrained Minimum Variance (LCMV) or others.

Another variation of the above-discussed exemplary embodiments concernsthe use of passive sources instead of active sources. In this context,the point-to-point traces between two receivers are built using acorrelation of the two receivers record. A description of such atechnique can be found in, e.g., U. Wegler and C. Sens-Schönfelder,“Fault Zone Monitoring with Passive Image Interferometry,” Geophys. J.Int., Vol. 168, 1029-1033, 2007.

The exemplary embodiments discussed in this application show that it ispossible to estimate near-surface relative temporal variations of a bodywave using the variations computed based on another body wave and/orsurface wave. With these novel algorithms, depth variations monitoringis made easier and finer. Extensions of these methods apply to otherparameters computed with any type of beamforming techniques.

The novel algorithms discussed above may be implemented in a computingsystem. An example of a representative computing system capable ofcarrying out operations in accordance with the exemplary embodiments isillustrated in FIG. 15. Hardware, firmware, software or a combinationthereof may be used to perform the various steps and operationsdescribed herein.

The exemplary computing system 1500 suitable for performing theactivities described in the exemplary embodiments may include server1501. Such a server 1501 may include a central processor (CPU) 1502coupled to a random access memory (RAM) 1504 and to a read-only memory(ROM) 1506. The ROM 1506 may also be other types of storage media tostore programs, such as programmable ROM (PROM), erasable PROM (EPROM),etc. The processor 1502 may communicate with other internal and externalcomponents through input/output (I/O) circuitry 1508 and bussing 1510,to provide control signals and the like. The processor 1502 carries outa variety of functions as are known in the art, as dictated by softwareand/or firmware instructions.

The server 1501 may also include one or more data storage devices,including hard disk drives 1512, CD-ROM and/or DVD drives 1514, andother hardware capable of reading and/or storing information such asDVD, etc. In one embodiment, software for carrying out theabove-discussed steps may be stored and distributed on a CD-ROM 1516,removable media 1518 or other form of media capable of portably storinginformation. These storage media may be inserted into, and read by,devices such as the CD-ROM drive 1514, the disk drive 1512, etc. Theserver 1501 may be coupled to a display 1520, which may be any type ofknown display or presentation screen, such as LCD, LED displays, plasmadisplay, cathode ray tubes (CRT), etc. A user input interface 1522 isprovided, including one or more user interface mechanisms such as amouse, keyboard, microphone, touch pad, touch screen, voice-recognitionsystem, etc.

The server 1501 may be coupled to other computing devices, such aslandline and/or wireless terminals via a network. The server may be partof a larger network configuration as in a global area network (GAN) suchas the Internet 1528, which allows ultimate connection to the variouslandline and/or mobile client/watcher devices.

As also will be appreciated by one skilled in the art, the exemplaryembodiments may be embodied in a wireless communication device, atelecommunication network, as a method or in a computer program product.Accordingly, the exemplary embodiments may take the form of an entirelyhardware embodiment or an embodiment combining hardware and softwareaspects. Further, the exemplary embodiments may take the form of acomputer program product stored on a computer-readable storage mediumhaving computer-readable instructions embodied in the medium. Anysuitable computer readable medium may be utilized including hard disks,CD-ROMs, digital versatile disc (DVD), optical storage devices, ormagnetic storage devices such a floppy disk or magnetic tape. Othernon-limiting examples of computer-readable media include flash-typememories or other known memories.

The disclosed exemplary embodiments provide a system and a method forcomputing/estimating relative temporal variations due to changes in apredetermined underground structure. It should be understood that thisdescription is not intended to limit the invention. On the contrary, theexemplary embodiments are intended to cover alternatives, modificationsand equivalents, which are included in the spirit and scope of theinvention as defined by the appended claims. Further, in the detaileddescription of the exemplary embodiments, numerous specific details areset forth in order to provide a comprehensive understanding of theclaimed invention. However, one skilled in the art would understand thatvarious embodiments may be practiced without such specific details.

Although the features and elements of the present exemplary embodimentsare described in the embodiments in particular combinations, eachfeature or element can be used alone without the other features andelements of the embodiments or in various combinations with or withoutother features and elements disclosed herein.

This written description uses examples of the subject matter disclosedto enable any person skilled in the art to practice the same, includingmaking and using any devices or systems and performing any incorporatedmethods. The patentable scope of the subject matter is defined by theclaims, and may include other examples that occur to those skilled inthe art. Such other examples are intended to be within the scope of theclaims.

What is claimed is:
 1. A method for calculating in a computing device acorrected temporal variation (dt₁)_(depth) or a corrected relativetemporal variation (dt₁/t₁)_(depth) of a body wave based on a givenwave, the method comprising: receiving raw seismic data recorded with areceiver, wherein the raw seismic data includes recordings for the bodywave and the given wave at various times; calculating a firstarrival-time variation (dt₁) for the body wave, wherein the firstarrival-time variation (dt₁) is obtained by comparing a travel-time ofthe body wave recorded at a first recording time (t₁) with a firstreference travel-time associated with the body wave; calculating asecond arrival-time variation (dt₂) for the given wave, wherein thesecond arrival-time variation (dt₂) is obtained by comparing atravel-time of the given body wave recorded at a second recording time(t₂) with a second reference travel-time associated with the given bodywave; calculating a first relative temporal variation (dt₁/t₁) for thebody wave based on the first arrival-time variation (dt₁), wherein thefirst relative temporal variation (dt₁/t₁) is a ratio between the firstarrival-time variation (dt₁) and the first recording time (t₁);calculating a second relative temporal variation (dt₂/t₂) for the givenwave based on the second arrival-time variation (dt₂), wherein thesecond relative temporal variation (dt₂/t₂) is a ratio between thesecond arrival-time variation (dt₂) and the second recording time (t₂)different from the first recording time (t₁) correcting with thecomputing device the first relative temporal variation (dt₁/t₁) based onthe second relative temporal variation (dt₂/t₂) to obtain the correctedrelative temporal variation (dt₁/t₁)_(depth), or correcting with thecomputing device the first arrival-time variation (dt₁) based on thesecond arrival-time variation (dt₂) to obtain the corrected temporalvariation (dt₁)_(depth); and monitoring changes in a target present in asubstrate based on the corrected relative temporal variation(dt₁/t₁)_(depth) or the corrected temporal variation (dt₁)_(depth),wherein a body wave is a wave that experiences at least one reflectionbefore being recorded by the receiver, a surface wave is a wave thatdoes not experience any reflection between the source and the receiver,and the given wave is either the body wave or the surface wave.
 2. Themethod of claim 1, wherein the step of calculating the firstarrival-time variation (dt₁) for the body wave comprises: calculating adifference between a travel-time of the body wave recorded at a firsttime (t1) and a travel-time of the body wave recorded at a second time(t2) to determine the first arrival-time variation (dt₁).
 3. The methodof claim 1, wherein the step of calculating the second arrival-timevariation (dt₂) for the surface wave comprises: calculating a differencebetween a travel-time of the given wave recorded at the second recordingtime (t2) and a reference travel-time of the given wave recorded at adifferent time to determine the second arrival-time variation (dt₂). 4.The method of claim 1, wherein the body wave is reflected from asubsurface target formation.
 5. The method of claim 1, wherein the firstbody wave and the given wave experience a same velocity variation in alayer having a lesser depth than a subsurface target formation.
 6. Themethod of claim 5, wherein the layer is a given layer affected byenvironmental changes.
 7. The method of claim 1, wherein the step ofcalculating the first relative temporal variation (dt₁/t₁) for the bodywave comprises: dividing the first arrival-time variation (dt₁) by afirst recording time (t₁).
 8. The method of claim 1, wherein the step ofcalculating the second relative temporal variation (dt₂/t₂) for thegiven wave comprises: dividing the second arrival-time variation (dt₂)by a second recording time (t₂).
 9. The method of claim 1, furthercomprising: applying an array processing algorithm to the raw seismicdata for separating recorded waves into the body wave and the givenwave.
 10. The method of claim 1, wherein the step of correctingcomprises: calculating the corrected relative temporal variation(dt₁/t₁)_(depth) based on,${\left( \frac{d\; t_{1}}{t_{1}} \right)_{depth} = {\left( \frac{d\; t_{1}}{t_{1}} \right) - {r \cdot \left( \frac{d\; t_{2}}{t_{2}} \right)}}},$where r is a constant that is determined from a graph in which the firstrelative temporal variation is plotted against the second relativetemporal variation, t₁ is a first recording time and t₂ is a secondrecording time.
 11. The method of claim 10, wherein data in the graphthat produces an r substantially around 1 is not considered.
 12. Themethod of claim 10, wherein r is calculated using a mathematical methodthat fits a line to the considered data.
 13. The method of claim 1,further comprising: calculating a relative velocity variation (dV/V) inthe subsurface based on the corrected relative temporal variation(dt₁/t₁)_(depth).
 14. The method of claim 1, further comprising:monitoring changes in a target present in a substrate based on thecorrected relative temporal variation (dt₁/t₁)_(depth).
 15. A method forcalculating in a computer device a corrected parameter variation(dp₁)_(depth) or a corrected relative parameter variation(dp₁/t₁)_(depth) of a body wave based on a given wave, the methodcomprising: receiving raw seismic data recorded with a receiver, whereinthe raw seismic data includes recordings for the body wave and the givenwave at various times; applying an array processing algorithm todetermine first and second relative parameter variations (dp₁/t₁) and(dp₂/t₂), wherein the first relative parameter variation (dp₁/t₁) is aratio between the first parameter variation (dp₁) and a first recordingtime (t₁) and the second relative parameter variation (dp₂/t₂) is aratio between a second parameter variation (dp₂) and a second recordingtime (t₂); correcting the first relative parameter variation (dp₁/t₁)based on the second relative parameter variation (dp₂/t₂) to obtain thecorrected relative parameter variation (dp₁/t₁)_(depth), or correctingthe first parameter variation (dp₁) based on the second parametervariation (dp₂) to obtain the corrected parameter variation(dp₁)_(depth), and monitoring changes in a target present in a substratebased on the corrected relative parameter variation (dp₁/t₁)_(depth) orthe corrected temporal variation (dt₁)_(depth), wherein a body wave is awave that experiences at least one reflection before being recorded bythe receiver, a surface wave is a wave that does not experience anyreflection, and the given wave is either the body wave or the surfacewave, wherein the first parameter variation (dp₁) is obtained bycomparing a first parameter of the body wave recorded at a first time(t₁) with a first reference parameter associated with the body wave, andwherein the second parameter variation (dp₂) is obtained by comparing afirst parameter of the given wave recorded at a second time (t₂) with asecond reference parameter associated with the given wave.
 16. Themethod of claim 15, wherein the parameter p is one of a travel-time,amplitude, slowness or incidence of source, slowness or incidence of thereceiver, source azimuth or receiver azimuth.
 17. A computing device forcalculating a corrected temporal variation (dt₁)_(depth) or a correctedrelative temporal variation (dt₁/t₁)_(depth) of a body wave based on agiven wave, the computing device comprising: an interface configured toreceive raw seismic data recorded with a receiver, wherein the rawseismic data includes recordings for the body wave and the given wave atvarious times; and a processor connected to the interface and configuredto, calculate a first arrival-time variation (dt₁) for the body wave,wherein the first arrival-time variation (dt₁) is obtained by comparinga travel-time of the body wave recorded at a first time (t₁) with afirst reference travel-time associated with the body wave, calculate asecond arrival-time variation (dt₂) for the given wave, wherein thesecond arrival-time variation (dt₂) is obtained by comparing atravel-time of the given wave recorded at the second time (t₂) with asecond reference travel-time associated with the given wave, calculate afirst relative temporal variation (dt₁/t₁) for the body wave based onthe first arrival-time variation (dt₁), wherein the first relativetemporal variation (dt₁/t₁) is a ratio between the first arival-timevariation (dt₁) and a first recording time (t₁), calculate a secondrelative temporal variation (dt₂/t₂) for the given wave based on thesecond arrival-time variation (dt₂), wherein the second relativetemporal variation (dt₂/t₂) is a ratio between the second arrival-timevariation (dt₂) and a second recording time (t₂), correct the firstrelative temporal variation (dt₁/t₁) based on the second relativetemporal variation (dt₂/t₂) to obtain the corrected relative temporalvariation (dt₁/t₁)_(depth), or correct the first temporal variation(dt₁) based on the second temporal variation (dt₂) to obtain thecorrected temporal variation (dt₁)_(depth), and monitoring changes in atarget present in a substrate based on the corrected relative temporalvariation (dt₁/t₁)_(depth) or the corrected temporal variation(dt₁)_(depth), wherein the body wave is a wave that experiences at leastone reflection before being recorded by the receiver, a surface wave isa wave that does not experience any reflection, and the given wave iseither the body wave or the surface wave.
 18. The computing device ofclaim 17, wherein the body wave is reflected from a sub given targetformation.
 19. The computing device of claim 17, wherein the body waveand the given wave experience a same velocity variation in a layerhaving a lesser depth than a subsurface target formation.
 20. Thecomputing device of claim 19, wherein the layer is a surface layeraffected by environmental changes.
 21. The computing device of claim 17,wherein the processor is further configured to: apply an arrayprocessing algorithm to the raw seismic data for separating recordedwaves into the body wave and the given wave.
 22. The computing device ofclaim 17, wherein the processor is further configured to: calculate thecorrected relative temporal variation (dt₁/t₁)_(depth) based on,${\left( \frac{d\; t_{1}}{t_{1}} \right)_{depth} = {\left( \frac{d\; t_{1}}{t_{1}} \right) - {r \cdot \left( \frac{d\; t_{2}}{t_{2}} \right)}}},$where r is a constant that is determined from a graph in which the firstrelative temporal variation is plotted against the second relativetemporal variation, t₁ is a first recording time and t₂ is a secondrecording time.
 23. A computer-readable storing medium includingcomputer executable instructions, wherein the instructions, whenexecuted by a processor, implement instructions for calculating acorrected temporal variation (dt₁)_(depth) or a corrected relativetemporal variation (dt₁/t₁)_(depth) of a body wave based on a givenwave, the instructions comprising: receiving raw seismic data recordedwith a receiver, wherein the raw seismic data includes recordings forthe body wave and the given wave at various times; calculating a firstarrival-time variation (dt₁) for the body wave, wherein the firstarival-time variation (dt₁) is obtained by comparing a travel-time ofthe body wave recorded at a first time (t₁) with a first referencetravel-time associated with the body wave; calculating a secondarrival-time variation (dt₂) for the given wave, wherein the secondarival-time variation (dt₂) is obtained by comparing a travel-time ofthe given wave recorded at a second time (t₂) with a second referencetravel-time associated with the given wave; calculating a first relativetemporal variation (dt₁/t₁) for the body wave based on the firstarrival-time variation (dt₁), wherein the first relative temporalvariation (dt₁/t₁) is a ratio between the first arival-time variation(dt₁) and a first recording time (t₁); calculating a second relativetemporal variation (dt₂/t₂) for the given wave based on the secondarrival-time variation (dt₂), wherein the second relative temporalvariation (dt₂/t₂) is a ratio between the second arrival-time variation(dt₂) and a second recording time (t₂); correcting in a computing devicethe first relative temporal variation (dt₁/t₁) based on the secondrelative temporal variation (dt₂/t₂) to obtain the corrected relativetemporal variation (dt₁/t₁)_(depth), or correcting in the computingdevice the first temporal variation (dt₁) based on the second temporalvariation (dt₂) to obtain the corrected temporal variation(dt₁)_(depth), and monitoring changes in a target present in a substratebased on the corrected relative temporal variation (dt₁/t₁)_(depth) orthe corrected temporal variation (dt₁)_(depth), wherein the body wave isa wave that experiences at least one reflection before being recorded bythe receiver, a surface wave is a wave that experiences no reflectionand the given wave is either the body wave or the surface wave.